The performance of a digital communication process in a mobile or wireless radio transmission system is constrained by non-ideal characteristics of the transmission channel whose main impairments are additive noise and intersymbol interference caused by multipath propagation in the available bandwidth. Thus, fading environments and presence of interference lead to high transmission error probabilities. Most radio systems have error rates of around 10−2 (i.e. one out of hundred bits is erroneous). The simplest way to reduce this error rate would be to increase the transmitted power such that even in a deep fade there would still be sufficient signal strength to reliably receive the signal. However, this would just increase the interference to the next cell, resulting in an increased error rate in that cell. An alternative, and better, approach is to add redundancy to the transmitted signal. This has the effect of increasing the bandwidth required for transmission but allowing the receiver to use knowledge of the redundancy to remove errors. This tradeoff of bandwidth for decreased error rates is the basis of error correction systems.
There are two different types of error control systems, those based on block coding and those based on convolutional coding. Both work by adding extra information to the data to be transmitted and then using a knowledge of the redundancy in order to correct errors in the original data. The difference between block and convolutional coding is the manner in which the redundancy is added. Block codes add a block of extra data after the information to be transmitted. Convolutional codes modify the data itself, adding redundancy in the process.
BER (Bit Error Rate) evaluation is usually done in GSM systems using a convolutional decoder output information, wherein decoded bits are encoded again using the same polynomials. The obtained two bit vectors are compared bit by bit. The difference between these e.g. 456 bit long vectors implies the actual BER. This method is called pseudo BER.
The BER may be evaluated in the receiver of wireless telecommunication systems in order to obtain a good estimate of BER required for example in radio link adaptation and speech and data service quality estimation. However, the above pseudo BER leads to the problem that the required convolutional coding and decoding are time-consuming operations which require a large amount of program and data memory.